Kernel methods enable the direct usage of structured representations of textual data during language learning and inference tasks. On the other side, deep neural networks are effective in learning nonlinear decision functions. Recent works demonstrated that expressive kernels and deep neural networks can be combined in a Kernel-based Deep Architecture (KDA), a common framework that allows to explicitly model structured information into a neural network. This combination achieves state-of-the-art accuracy in different semantic inference tasks. This paper investigates the impact of linguistic information on the performance reachable by a KDA by studying the benefits that different kernels can bring to the inference quality. We believe that the expressiveness of data representations will play a key role in the wide spread adoption of neural networks in AI problem solving. We experimentally evaluated the adoption of different kernels (each characterized by a growing expressive power) in a Question Classification task. Results suggest the importance of rich kernel functions in optimizing the accuracy of a KDA.
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